import geopandas as gpdGeoPandas
import pandas as pdgdf = gpd.read_parquet("~/data/scag_region.parquet")gdf.shape(4580, 194)
type(gdf)geopandas.geodataframe.GeoDataFrame
gdf.head()| geoid | n_asian_under_15 | n_black_under_15 | n_hispanic_under_15 | n_native_under_15 | n_white_under_15 | n_persons_under_18 | n_asian_over_60 | n_black_over_60 | n_hispanic_over_60 | ... | year | n_total_housing_units_sample | p_nonhisp_white_persons | p_white_over_60 | p_black_over_60 | p_hispanic_over_60 | p_native_over_60 | p_asian_over_60 | p_disabled | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 06037128702 | 58.0 | 0.0 | 223.0 | 0.0 | 475.0 | 986.0 | NaN | NaN | NaN | ... | 2010 | 2903.0 | 64.726214 | NaN | NaN | NaN | NaN | NaN | NaN | POLYGON ((-118.44870 34.16485, -118.43997 34.1... |
| 1 | 06037131600 | 83.0 | 62.0 | 777.0 | 0.0 | 135.0 | 1355.0 | NaN | NaN | NaN | ... | 2010 | 1487.0 | 28.679979 | NaN | NaN | NaN | NaN | NaN | NaN | POLYGON ((-118.56229 34.22033, -118.55792 34.2... |
| 2 | 06037134104 | 287.0 | 17.0 | 816.0 | 0.0 | 61.0 | 1323.0 | NaN | NaN | NaN | ... | 2010 | 1388.0 | 14.846188 | NaN | NaN | NaN | NaN | NaN | NaN | POLYGON ((-118.57976 34.21558, -118.57539 34.2... |
| 3 | 06037134304 | 90.0 | 24.0 | 298.0 | 0.0 | 89.0 | 520.0 | NaN | NaN | NaN | ... | 2010 | 928.0 | 33.378933 | NaN | NaN | NaN | NaN | NaN | NaN | POLYGON ((-118.61472 34.21952, -118.61039 34.2... |
| 4 | 06037242000 | 0.0 | 229.0 | 681.0 | 0.0 | 0.0 | 1164.0 | NaN | NaN | NaN | ... | 2010 | 1054.0 | 0.058565 | NaN | NaN | NaN | NaN | NaN | NaN | POLYGON ((-118.25416 33.93882, -118.25413 33.9... |
5 rows × 194 columns
gdf.columns.valuesarray(['geoid', 'n_asian_under_15', 'n_black_under_15',
'n_hispanic_under_15', 'n_native_under_15', 'n_white_under_15',
'n_persons_under_18', 'n_asian_over_60', 'n_black_over_60',
'n_hispanic_over_60', 'n_native_over_60', 'n_persons_over_60',
'n_white_over_60', 'n_asian_over_65', 'n_black_over_65',
'n_hispanic_over_65', 'n_native_over_65', 'n_white_over_65',
'n_persons_over_75', 'n_persons_over_15', 'n_civilians_over_16',
'n_civilians_over_18', 'n_persons_over_25', 'n_age_5_older',
'n_asian_age_distribution', 'n_black_age_distribution',
'n_hispanic_age_distribution', 'n_native_age_distribution',
'n_white_age_distribution', 'n_asian_persons', 'n_black_persons',
'n_chinese_persons', 'n_labor_force', 'n_civilians_16_64',
'n_edu_college_greater', 'n_cuban_pop',
'n_poverty_determined_asian', 'n_poverty_determined_black',
'n_total_pop_sample', 'n_female_over_16',
'n_poverty_determined_families', 'n_poverty_determined_hispanic',
'n_disabled', 'n_housing_units_multiunit_structures_denom',
'n_poverty_determined_native', 'n_poverty_determined_persons',
'n_poverty_determined_white', 'n_employed_over_16',
'n_total_families', 'n_foreign_born_pop',
'n_female_headed_families', 'n_filipino_persons',
'n_female_labor_force', 'n_german_pop', 'n_german_born_pop',
'n_household_recent_move', 'n_structures_30_old',
'n_hawaiian_persons', 'n_total_households', 'n_asian_households',
'n_black_households', 'n_hispanic_households',
'n_white_households', 'median_household_income',
'median_income_asianhh', 'median_income_blackhh',
'median_income_hispanichh', 'median_income_whitehh',
'n_hispanic_persons', 'n_edu_hs_less', 'n_total_housing_units',
'per_capita_income', 'n_asian_indian_persons', 'n_irish_pop',
'n_irish_born_pop', 'n_italian_pop', 'n_italian_born_pop',
'n_japanese_persons', 'n_korean_persons', 'n_limited_english',
'n_employed_manufacturing', 'n_married', 'n_mexican_pop',
'median_home_value', 'median_contract_rent',
'n_housing_units_multiunit_structures', 'n_recent_immigrant_pop',
'n_poverty_over_65', 'n_poverty_asian', 'n_naturalized_pop',
'n_poverty_black', 'n_poverty_families_children',
'n_nonhisp_black_persons', 'n_poverty_hispanic',
'n_nonhisp_white_persons', 'n_poverty_native', 'n_poverty_persons',
'n_native_persons', 'n_poverty_white', 'n_occupied_housing_units',
'n_other_language', 'n_owner_occupied_housing_units',
'p_recent_immigrant_pop', 'p_household_recent_move',
'p_asian_under_15', 'p_black_under_15', 'p_hispanic_under_15',
'p_native_under_15', 'p_white_under_15', 'p_persons_under_18',
'p_structures_30_old', 'p_persons_over_60', 'p_asian_over_65',
'p_black_over_65', 'p_hispanic_over_65', 'p_native_over_65',
'p_poverty_rate_over_65', 'p_white_over_65', 'p_persons_over_75',
'p_poverty_rate_asian', 'p_asian_persons', 'p_poverty_rate_black',
'p_chinese_persons', 'p_edu_college_greater', 'p_cuban_pop',
'p_foreign_born_pop', 'p_female_headed_families',
'p_filipino_persons', 'p_female_labor_force',
'p_poverty_rate_children', 'p_german_pop', 'p_german_born_pop',
'p_hawaiian_persons', 'p_hispanic_persons',
'p_poverty_rate_hispanic', 'p_edu_hs_less',
'p_asian_indian_persons', 'p_irish_pop', 'p_irish_born_pop',
'p_italian_pop', 'p_italian_born_pop', 'p_japanese_persons',
'p_korean_persons', 'p_limited_english',
'p_employed_manufacturing', 'p_married', 'p_mexican_pop',
'p_housing_units_multiunit_structures', 'p_poverty_rate_native',
'p_naturalized_pop', 'p_nonhisp_black_persons', 'p_black_persons',
'p_native_persons', 'p_other_language', 'n_total_pop',
'p_owner_occupied_units', 'p_poverty_rate', 'p_puerto_rican_pop',
'p_employed_professional', 'n_puerto_rican_pop',
'n_employed_professional', 'p_russian_pop', 'p_russian_born_pop',
'p_scandanavian_pop', 'p_scandanavian_born_pop',
'p_employed_self_employed', 'p_unemployment_rate',
'p_vacant_housing_units', 'p_veterans', 'p_vietnamese_persons',
'p_widowed_divorced', 'p_poverty_rate_white',
'n_renter_occupied_housing_units', 'n_russian_pop',
'n_russian_born_pop', 'n_scandaniavian_pop',
'n_scandaniavian__born_pop', 'n_employed_self_employed',
'n_unemployed_persons', 'n_vacant_housing_units', 'n_veterans',
'n_vietnamese_persons', 'n_widowed_divorced', 'n_white_persons',
'year', 'n_total_housing_units_sample', 'p_nonhisp_white_persons',
'p_white_over_60', 'p_black_over_60', 'p_hispanic_over_60',
'p_native_over_60', 'p_asian_over_60', 'p_disabled', 'geometry'],
dtype=object)
gdf.n_total_pop0 5497.0
1 5659.0
2 4486.0
3 2924.0
4 3415.0
...
4575 3672.0
4576 5257.0
4577 6765.0
4578 2981.0
4579 3994.0
Name: n_total_pop, Length: 4580, dtype: float64
gdf.geometry0 POLYGON ((-118.44870 34.16485, -118.43997 34.1...
1 POLYGON ((-118.56229 34.22033, -118.55792 34.2...
2 POLYGON ((-118.57976 34.21558, -118.57539 34.2...
3 POLYGON ((-118.61472 34.21952, -118.61039 34.2...
4 POLYGON ((-118.25416 33.93882, -118.25413 33.9...
...
4575 POLYGON ((-118.50373 34.42607, -118.50050 34.4...
4576 POLYGON ((-118.20731 33.90754, -118.20641 33.9...
4577 POLYGON ((-119.22134 34.18130, -119.21727 34.1...
4578 POLYGON ((-116.51068 33.80502, -116.51069 33.8...
4579 POLYGON ((-118.41379 34.17940, -118.41160 34.1...
Name: geometry, Length: 4580, dtype: geometry
gdf.plot()<Axes: >

Projections
gdf.crs<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
gdf1 = gdf.to_crs(3857)gdf1.crs<Projected CRS: EPSG:3857>
Name: WGS 84 / Pseudo-Mercator
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: World between 85.06°S and 85.06°N.
- bounds: (-180.0, -85.06, 180.0, 85.06)
Coordinate Operation:
- name: Popular Visualisation Pseudo-Mercator
- method: Popular Visualisation Pseudo Mercator
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
gdf1.plot()<Axes: >

gdf.median_home_value0 647272.659176
1 400842.977528
2 416741.666667
3 406178.838951
4 251438.857678
...
4575 291838.951311
4576 273871.254682
4577 293254.588015
4578 255794.662921
4579 581717.790262
Name: median_home_value, Length: 4580, dtype: float64
gdf.plot(column='median_home_value')<Axes: >

gdf.plot(column='median_home_value', legend=True)<Axes: >

gdf.plot(column='median_home_value', legend=True,
scheme='quantiles', k=10)<Axes: >

gdf.plot(column='p_hispanic_persons', legend=True,
scheme='quantiles', k=10)<Axes: >

gdf.explore(column='p_hispanic_persons', tooltip=['geoid', 'p_hispanic_persons'])Make this Notebook Trusted to load map: File -> Trust Notebook